Modern vehicle navigation systems use several different types of sensors to measure the same or related information for navigation purposes. Examples of such types of sensors are the differential odometer, compass, altimeter, steering angle sensor, gyroscope, differential accelerometer, GPS receiver, and CD ROM, or semiconductor memory based digital map database, to consider a few. Vehicular navigation by dead-reckoning, or odometry, relying on a differential odometer is commonplace. We will first look at the differential odometer to exemplify the problem with current technology.
A differential odometer is typically constructed with a pair of pulse counting sensors each of which are mounted on laterally opposite sides of the vehicle to the non-powered wheels. As the vehicle moves and the wheels rotate, pulses are generated, the incremental number indicating distance traversed. The difference in pulses generated between the pair of sensors is indicative of the change in heading. While navigating by dead-reckoning, the vehicle will develop a location uncertainty that increases during operation due to various error sources inherent in the behavior of an incremental positioning system. These error sources include systematic errors, gross errors, and random errors. If uncorrected these errors are substantial and make the navigation system impractical. We now look at some error sources in more detail.
Errors are systematic in nature when they are consistently repeatable and expected by the nature of the sensor in response to installation and/or vehicle dynamics. In the case of the differential odometer, as the vehicle travels, or traverses, an unknown variation in tire circumference will yield a difference in the rate output from the pair of sensors. This difference, or change in heading, manifests itself in a sensed circular pattern, indicating to the observer that the vehicle is turning when it isn't. The error contributable to this circular behavior can be significant. Experience shows cases of differences in position caused by cumulative heading errors of 200 meters per kilometer.
Another example of a systematic error can be illustrated when a differential odometer is applied to the non-powered front, or steered wheels, of a rear wheel drive vehicle. In this case the differential odometer suffers from tracking error when the vehicle turns, as the distance between the tires is changing, yielding heading errors. Other tire circumference variations, resulting from wear, centrifugal forces resulting from speed variation, and pressure variations resulting from temperature effects, all contribute to systematically erroneous behavior.
Other sources of systematic error include terrain aberrations, or altitude changes resulting from hills causing distance traversed errors when compared to a map, and ground surface to mapping plane transformation errors, resulting from the curvature of the earth. Of course other sensors characteristically have systematic errors. In a compass they include: axis offset installation, compass plane not being parallel to the source magnetic field, for instance when driving on a slope, and vehicle internal magnetic anomalies. An added compass error is found when one considers the declination angle between geographic north and magnetic north. At compass installation this can be inherently corrected by proper installation, however when traversing, this declination angle dynamically changes resulting from the curvature of the earth. Systematic errors characteristic of a GPS receiver include: inaccuracy resulting from satellite ephemeris, and partial availability of satellites. For GPS receivers, systematic errors include inaccurate modeling of the atmospheric effects. Digital maps exhibit systematic error with partial availability and precision of position coordinates. Generalized systematic errors from a sensory system include sensor synchronization problems, resulting from the asynchronous availability of information from distinct sensory devices. For instance, in a GPS sensor the available position information is updated asynchronously and relatively infrequently compared to the differential odometer, requiring interpolation of absolute location systematically adding error.
Gross errors are characteristic of the sensory interaction with the vehicle's behavior, and are typically transient or noncontinuous in nature. Differential odometer examples include wheel slippage including lateral slip, resulting from surface potholes, or differing surface friction coefficients such as one wheel on ice and the other on firm ground, or skidding on a turn maneuver. Gross errors in a compass include magnetic blunders, resulting from a temporary interfering magnetic field, such as expected when traversing a metal bridge with a magnetic bias. Gross errors in a GPS receiver include multi-path reflections from buildings increasing pseudo-ranges.
Random errors are more likely to average to zero over time. Because of the nature of random errors correlation between adjacent sensor readings cannot be reliably established. Sensor readings can result in any value. The time of occurrence and discontinuation of a random error is unpredictable. An example of random error in a differential odometer is the missing of pulses, resulting from a variety of reasons including electromagnetic interference or loose electrical connections. A random compass error occurs when it gets disturbed from its mount by driving on a rough road. GPS receiver random errors are inherent in the pseudo-range measurement.
This list is not meant to be exhaustive or exclusive but represents the many error sources that a vehicle navigation system needs to consider when deriving accurate vehicle location, position and heading variables.
Reference art teaches correction of location errors by a variety of map matching schemes. These include correction after turning from one road segment to the next, and probabilistic solutions. The latter are accomplished by assigning a probability to adjacent map segments based on their relationship to the sensed motion of the vehicle, then selecting the most probable segment based on these probability assignments. Although this technique may work in a crude manner, it fails miserably when the operator transits off the map or to a new road segment contained within the map domain but not identified. Others have tried to recognize and correct for certain error types with an analytical approach. These techniques are characteristically computation intensive, requiring a high level of resource to operate. These attempts don't address many error sources, and only addresses a small set of problems infrequently.
In summary, vehicular navigation relies on various navigation parameters provided by sensors and map information to derive current location, position, and heading variables. This information is known to have erroneous behavior and is not continuously available. This yields a certain uncertainty in the vehicle location, position, and heading variables. Analytical, or crisp, models have failed to provide a good fit to describe the nature of all erroneous behavior. Hence, excessively high precision and generous error windows are applied to assure vehicle location, position, and heading information taxing computational and memory resources. These analytical models also have a low tolerance to unforeseen exception conditions that may severely degrade system performance and product quality.